320 lines
14 KiB
Python
320 lines
14 KiB
Python
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# coding=utf-8
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License
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""" Tokenization classes for Camembert model."""
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
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SPIECE_UNDERLINE = "▁"
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class CamembertTokenizer(PreTrainedTokenizer):
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"""
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Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Construct a CamemBERT tokenizer. Based on
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[SentencePiece](https://github.com/google/sentencepiece).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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additional_special_tokens (`List[str]`, *optional*, defaults to `['<s>NOTUSED', '</s>NOTUSED', '<unk>NOTUSED']`):
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Additional special tokens used by the tokenizer.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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Attributes:
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sp_model (`SentencePieceProcessor`):
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED", "<unk>NOTUSED"],
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs,
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) -> None:
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# Mask token behave like a normal word, i.e. include the space before it
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mask_token = (
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AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False, special=True)
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if isinstance(mask_token, str)
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else mask_token
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)
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(str(vocab_file))
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self.vocab_file = vocab_file
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# HACK: These tokens were added by the author for an obscure reason as they were already part of the
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# sentencepiece vocabulary (this is the case for <s> and </s> and <unk>).
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# In this case it is recommended to properly set the tokens by hand.
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self._added_tokens_decoder = {
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0: AddedToken("<s>NOTUSED", special=True),
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1: AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token,
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2: AddedToken("</s>NOTUSED", special=True),
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3: AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token,
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4: AddedToken("<unk>NOTUSED", special=True),
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}
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self.fairseq_offset = 4 # 3 tokens are newly added, but the offset starts from 4
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# legacy: camemebert is a particular case were we have to make sure `"<unk>NOTUSED"` is here
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if "added_tokens_decoder" in kwargs:
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# this is the only class that requires this unfortunately.....
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# the reason is that the fast version has a whole.
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kwargs["added_tokens_decoder"].update(self._added_tokens_decoder)
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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additional_special_tokens=additional_special_tokens,
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sp_model_kwargs=self.sp_model_kwargs,
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**kwargs,
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)
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@property
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def vocab_size(self):
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# The length of the vocabulary without added tokens is len(self.sp_model) but the added tokens are added at the beginning.
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return len(self.sp_model)
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.fairseq_offset)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text: str) -> List[str]:
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return self.sp_model.encode(text, out_type=str)
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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# specifi to camembert, both 3 and 4 point to the unk token.
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if self.sp_model.PieceToId(token) == 0:
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# Convert sentence piece unk token to fairseq unk token index
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return self.unk_token_id
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return self.fairseq_offset + self.sp_model.PieceToId(token)
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.sp_model.IdToPiece(index - self.fairseq_offset)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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# TODO decode outputs do not match between fast and slow
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current_sub_tokens = []
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out_string = ""
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prev_is_special = False
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special:
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out_string += " "
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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current_sub_tokens = []
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else:
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current_sub_tokens.append(token)
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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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return out_string.strip()
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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return state
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def __setstate__(self, d):
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(self.vocab_file)
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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out_vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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with open(out_vocab_file, "wb") as fi:
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content_spiece_model = self.sp_model.serialized_model_proto()
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fi.write(content_spiece_model)
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return (out_vocab_file,)
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. An CamemBERT sequence has the following format:
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- single sequence: `<s> X </s>`
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- pair of sequences: `<s> A </s></s> B </s>`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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cls = [self.cls_token_id]
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sep = [self.sep_token_id]
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return cls + token_ids_0 + sep + sep + token_ids_1 + sep
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. CamemBERT, like
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RoBERTa, does not make use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of zeros.
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"""
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
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